For much of the last year, I have been working with data collected from Intel’s Local Experiences of Automobility project.
“LEAM is a multi-disciplinary exploratory ethnographic research project focused on the diversity of automobility experiences in 3 of the world’s largest car markets in order to define a design space encompassing the car and the infrastructure and ecosystem around varied smart transportation futures. This design space allows us to generate multiple design possibilities and technical solutions around smart transportation and user interface solutions informed by locality and context dependent conditions of how, when, and why cars are used, by whom, and under what conditions. The diversity of the LEAM team – anthropologists, computer scientists, designers, an experimental psychologist and a data visualizer allows us to combine disparate data types and analyses in service of multi-faceted portraits of automobility grounded in an ethnographic perspective that privileges lived experiences of mobility. “
The LEAM project produced a wide variety of data: GPS tracking data, phone tracking data, data from a variety of in-car sensors, as well as rich data from ethnographic interviews. My goal was to extract insight from the quantitative data to both ground and inspire design. In addition to performing a variety of analyses on the data, I constructed two interactive visual tools and variety of static visualizations to illustrate a data model I developed to encapsulate the concept of how familiar a driver is with a particular point.
Disclaimer: The views expressed in this post are my own, not Intel’s.